Stats Central and the School of Mathematics and Statistics at UNSW are jointly conducting a short course, Statistical Methods for Research Workers, in November 2016. Aimed at research workers, the course provides an overview of statistical design and analysis methods. The course emphasises understanding the concepts underlying statistical procedures (relying on a minimum of mathematics) and interpreting the output from statistical analyses. The statistical package used in the course is R (click here for more information).
Introduction
Types of experiments, scales of measurement, which method to use.
Software
In this course the statistical software package used is R we do not use or demonstrate SPSS.
Summarising and Graphing Data
Ways of presenting data (histograms, boxplots), measures of centre and spread, analysing tables, correlation, and confidence intervals.
Comparing Groups
Hypothesis testing concepts-power, significance, P-value. Comparing two groups (t-tests, Wilcoxon). Comparing many groups - ANOVA or Kruskal-Wallis - multiple comparison tests, required sample size and repeated measures.
Finding Relationships
Correlation, predicting relationships (regression - simple).
Other topics covered:
· Survival analysis
· Linear mixed models for longitudinal and clustered data
· Generalised linear models (specifically logistic and Poisson regression)
Cost:
UNSW students: $225
UNSW staff: $450
External: $1500
Registration:
Registration is now open.
This Short Course is based on intellectual property developed by the School of Mathematics & Statistics.
Requirements:
Bring your own laptop computer.
Further information is available here.
FAQ
Q. Is the course offered for credit and will there be any assessment?
A. No, there is no credit for the course and there is no assessment. A certificate will be given to participants who complete the course.
Q. Is more detail on the course topics available?
A. In the "Other topics" section, the sub-topics are:
Survival analysis
Kaplan-Meier analysis, Cox proportional hazards models, checking the proportional hazards model
assumptions
Linear mixed models for longitudinal and clustered data
notes are built around a longitudinal example of a case-control study, spaghetti plots, model selection
using the AIC
Generalised linear models
logistic and Poisson regression, overdispersion, offsets